Mixed and latent Markov models as item response models

Rolf Langeheine has considerably contributed to the development of generalized Markov models. In particular, he is the one, who transposed the general approach of Markov chains into the framework of mixture distribution models which opened the field of theoretical extensions and possible applications drastically. Furthermore he addresses the distinction between manifest and latent Markov chains as an important property of this family of models when they were applied to real data. Real data typically are affected by error of measurement and, hence, should be treated by models allowing for those errors of measurement.